CN108195895A - A kind of tea leaf nitrogen content rapid detection method based on electronic nose and spectrophotometric color measurement instrument - Google Patents
A kind of tea leaf nitrogen content rapid detection method based on electronic nose and spectrophotometric color measurement instrument Download PDFInfo
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- CN108195895A CN108195895A CN201711437406.6A CN201711437406A CN108195895A CN 108195895 A CN108195895 A CN 108195895A CN 201711437406 A CN201711437406 A CN 201711437406A CN 108195895 A CN108195895 A CN 108195895A
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Abstract
The present invention relates to a kind of tea leaf nitrogen content rapid detection methods based on electronic nose and spectrophotometric color measurement instrument, by electronic nose sensor to the response difference and overloading analysis of tea leaf odoring substance, optimize and filter out 5 larger sensors of contribution rate.Next optimizes 3 principal elements of electronic nose detection architecture (gas collection vessel volume, head space time, head space temperature).Then the b in spectrophotometric color measurement instrument is determined by linear regression analysis*Value can indicate as judge Leaf nitrogen concentration one.With 5 main sensors of electronic nose and spectrophotometric color measurement instrument b*Value is characterized value and establishes the tea leaf nitrogen estimation model that electronic nose is combined with spectrophotometric color measurement instrument.By calculating, model modeling group accuracy rate is up to 95%, and validation group accuracy rate is up to 92.5%.
Description
Technical field
The present invention relates to a kind of tea leaf nitrogen content rapid detection methods based on electronic nose and spectrophotometric color measurement instrument, belong to
Plant nutrient field of fast detection, and in particular to a kind of to be combined quick detection tea leaf with spectrophotometric color measurement instrument using electronic nose
The method of nitrogen content.
Background technology
Electronic nose has response to complicated ingredient gas using each gas sensitive device but this different feature, simulation
Human olfactory system is identified a variety of smells by data processing method, so as to which smell quality is analyzed and be evaluated.
Electronic nose shows unique excellent in the character surveillance in the fields such as meat, tea wine class, fruits and vegetables class, quality evaluation and safety detection
Point, can all-the-way tracking processing technology, detection process online, to product without damage, rapid sensitive.Spectrophotometric color measurement instrument technology has inspection
Degree of testing the speed is fast, precision is high, is suitble to the features such as off-line operation in laboratory environment and production environment, and which employs spectrum analyses
Method carries out color measuring to object, can obtain accurate color data.Electronic nose combines other analytical instrument and carries out data
Convergence analysis is analysis method more popular at present.But it there is no be combined electronic nose with spectrophotometric color measurement instrument to identify tea at present
Set the report of Leaf nitrogen concentration.
Nitrogen is one of most important nutrient of tea tree.The diagnosis of plant formalness, total nitrogen diagnosis, plant nitric acid
Traditional tea tree nitrogen detection method operating process such as salt diagnosis is complicated, consumes more financial resources and lacks timeliness.As detection is new
The Nondestructives technology such as the emergence of technology, chlorophyll meter method, chlorophyll fluorescence techniques, remote sensing technology is gradually applied to tea
It sets in nitrogen diagnosis.In addition, the remote sensing technology based on object spectra reflectance signature identification object is also supervised in real time as plant nitrogen
Survey the possibility means with quick diagnosis.
Invention content
The present invention provides a kind of tea leaf nitrogen content rapid detection methods based on electronic nose and spectrophotometric color measurement instrument.
Inventor first by electronic nose (German PEN3 types) sensor to the response difference of tea leaf odoring substance with
And overloading analysis, optimize and filter out 5 larger sensors of contribution rate (W5S, W1S, W1W, W2S, W2W), in this, as rear
The main sensors of continuous detection.Detection architecture of the electronic nose to tea leaf is optimized later.Secondly, pass through linear regression analysis
The b in spectrophotometric color measurement instrument is determined*Value can indicate as judge Leaf nitrogen concentration one.With W5S, W1S, W1W,
The response (G/G0) and b of W2S, W2W*Value establishes the tea leaf nitrogen estimation mould that electronic nose is combined with spectrophotometric color measurement instrument
Type.Model is verified and predicted simultaneously, model modeling group accuracy rate is up to 95%, and validation group accuracy rate is up to 92.5%.
A kind of tea leaf nitrogen content rapid detection method based on electronic nose and spectrophotometric color measurement instrument, step are as follows：
(1) material prepares：20 not damaged tea tree mature leafs (the downward 3-4 pieces of terminal bud) are selected, are cleaned up, are wiped
It is dry；
(2) electronic nose detects：The ready blade of step 1) is put into 50mL gas collection bottles, is sealed with masking foil close
Envelope；Gas detection is carried out using electronic nose using headspace injection method.It is pre- that gas collection bottle is placed in progress head space in 30 DEG C of environment
Heat, head space time 30min.Detection parameters are：Sensor scavenging period 100s, automatic zero set time 10s, sample preparation time
5s, sample measuring interval time 1s, inner stream flow 300mL/min, sample introduction flow 300mL/min make the signal at 80-83s
For the time point of sensor signal analysis, W5S (very sensitive to oxynitrides), W1S are extracted respectively (to the spirit of methyl class compound
It is quick), W1W (sensitive to inorganic sulphide), W2S (sensitive to alcohols, aldoketones), W2W (fragrance ingredient, to organic sulfur compound spirit
It is quick) the response G/G0 of 5 sensors；
(3) spectrophotometric color measurement instrument detects：The ready blade of step 1) is fixed with specimen holder；Aberration is divided using CM-5
Instrument using LAB colour systems under d 65 illuminant (simulated solar irradiation), detects the b of 20 blades respectively*Value, takes b*It is worth average value
It substitutes into step 4) formula；
(4) by W5S, W1S, W1W, W2S, W2W response (G/G0) and b*Value substitutes into following equation, and the Y value obtained is maximum
Class value is judged to the LTN content.
In formula：G/G02、G/G06、G/G07、G/G08、G/G09W5S, W1S, W1W, W2S, W2W sensor are represented respectively,
Detection time is the signal response G/G0 at 80-83s.
The electronic nose is Germany's AIRSENSE companies-PEN3 type portable electric noses.
The tea leaf nitrogen content prediction model that the present invention establishes can accurately predict tea leaf nitrogen content.
Experiment Modeling group accuracy rate up to 95%, validation group accuracy rate can to 92.5%, can preferably to tea leaf nitrogen content into
Row prediction.
Description of the drawings
Fig. 1 is responded for different sensors and overloading analysis
Show that S2, S6, S7, S8, S9 are high to gaseous matter response in figure, contribution rate is big.(German PEN3 types portable electric
Sensor shares 10, respectively W1C, W5S, W3C, W6S, W5C, W1S, W1W, W2S, W2W, W3S in sub- nose, is marked in figure
S1-S10 represents above-mentioned 10 sensors successively)
Fig. 2 optimizes for electronic nose testing conditions
It is optimal detection body that gas collector volume 50mL, 30 DEG C of head space preheating temperature, head space time 30min are shown in figure
System.
Fig. 3 is tea leaf nitrogen content and electronic nose L*, a*, b* value linear regression analysis
L is shown in figure*It is worth, a minimum with blade total nitrogen content linear relationship*Although value shows certain linear, but determine
Coefficient is less than b*Value.b*Value can indicate as judge Leaf nitrogen concentration one.
Specific embodiment
The present invention for background, is established with conventional greenery tea tree breed (non-albefaction, purpleization kind) with reference to color difference meter, electronic nose
Model.There are albefaction and purpleization kind in tea tree breed, and albefaction kind and purpleization kind belong to leaf variegation kind, leaf color
White, yellow or purple is presented, aberration degree of variation is larger, is not belonging to conventional tea tree breed.Therefore, the leaf of this two big veriety
Chromatic variation is not in the use scope of this model.
A kind of tea leaf nitrogen content rapid detection method based on electronic nose and spectrophotometric color measurement instrument technology, method is such as
Under：
(1) tea leaf nitrogen content detects：Not damaged conventional greenery tea tree mature leaf (the downward 3-4 pieces of terminal bud) is taken,
After 105 DEG C finish 5-10 minutes, dried at 80 DEG C.It weighs drying, grind (0.25mm) 0.3000~0.5000g of sample, put
It is boiled in pipe in disappearing.The 8mL concentrated sulfuric acids are added in, gently shakes up, stands overnight.It will disappear to boil pipe and be placed in disappear to boil and be heated on stove, when solution is in
It is removed during uniform brownish black, adds in 10 drop hydrogen peroxide, shake up, be again heated to slightly boiling, disappear and boil about 5 minutes, removed and repeat to drip
Add using 10 drop hydrogen peroxide, then disappear and boil.It so repeats 3~5 times, disappears and boil solution in after colourless or limpid, remove filtering.It draws
2~5mL of filtered fluid measures nitrogen content using kjeldahl apparatus.
(2) electronic nose detection architecture optimizes：20 Tea Samples are put into and fill blotting paper and Na2CO3(blotting paper and
Na2CO3Dry after use) triangular flask in, with masking foil seal seal.It is portable using PEN3 types using headspace injection method
Electronic nose carries out gas detection, and records each sensor response G/G0 and (represent No. n-th sensor contacts to sample volatile matter
The ratio of resistance G afterwards and sensor resistance G0 of gas after being filtered by standard activity carbon).System optimization sets gas
Three receiving flask volume, head space preheating temperature, head space time factors.Wherein gas collection bottle Volume Test set 50,100,150mL
3 processing；The experiment of head space preheating temperature sets 30,40,50,60,70,80 DEG C of 6 processing；The experiment of head space time sets 5,10,15,
20th, 25,6 processing of 30min.Experiment is optimized to each processing.Electronic nose detection parameter is sensor scavenging period 100s,
Automatic zero set time 10s, sample preparation time 5s, sample measuring interval time 1s, inner stream flow 300mL/min, sample introduction flow
300mL/min, using the signal at 80-83s as the time point of sensor signal analysis.Before and after measuring every time, sensor carries out
Cleaning and standardization.Principal component analysis (PCA), linear discriminant analysis (LDA) and overload point are carried out using WinMuster softwares
Analysis filters out optimal gas collection bottle product, head space preheating temperature and head space time to carry out subsequent experiment.
Premenstruum (premenstrua) experiment finds that blade is very few, and the gas species concentration that blade volatilizes is too low, and the sensor of electronic nose is not
It is enough to carry out effectively identification to gas；If blade is excessive, leaf transpiration is acted on and can be produced when headspace enrichment gas
Raw a large amount of water vapours, the resolution ratio of sensor can reduce.It is measured so choosing 20 blades in the present invention.
(3) screening of aberration characteristic value：It is dried after tea leaf is cleaned up, and solid with specimen holder (equipment carries)
It is fixed.Using CM-5 light splitting color difference meter using LAB colour systems under d 65 illuminant (simulated solar irradiation), detect blade L*, a*,
(L* represents lightness to b* values；A* positive values are red, and negative value is green；B* positive values are Huang, and negative value is indigo plant).L*, a*, b* value are combined into (1)
Middle tea leaf nitrogen content carries out linear regression analysis, filters out optimal aberration characteristic value.
(4) tea leaf nitrogen content model foundation：With W5S, W1S, W1W, W2S, W2W (optimal sensing that step 2 screens
Device) response (G/G0) and b*Value (the optimal aberration characteristic value that step 3 screens) is utilized with reference to tea leaf nitrogen content
Matlab softwares establish prediction model.
It extracts above-mentioned 5 optimal sensors response (G/G0) at 80-83s and establishes prediction model with b* values.Based on gas
The LTN content prediction model of taste color combining is：
In formula, G/G02、G/G06、G/G07、G/G08、G/G09W5S, W1S, W1W, W2S, W2W sensor is represented respectively to exist
Detection time is the signal response G/G0 at 80-83s.
By G/G02、G/G06、G/G08、G/G07、G/G09, b* bring above formula into, the Y value maximum group obtained is judged to the blade
Nitrogen content.
Embodiment 1, by electronic nose detection architecture optimization for
PEN3 types general purpose transducer totally 10, respectively W1C, W5S, W3C, W6S, W5C, W1S, W1W, W2S, W2W, W3S.
The S1-S10 marked in Fig. 1 represents above-mentioned 10 sensors successively.Since 10 sensors stress the gas componant of detection not
Together, therefore electronic nose sensor has differences the response of tea leaf odoring substance.As shown in Figure 1, tri- biographies of S2, S7, S9
Sensor is all to gaseous matter response height, and certain response also occur in S6, S8 sensor, but response is less than S2, S7, S9.S1、
5 sensor responses of S3, S4, S5, S10 show that these sensors are insensitive to the response of gaseous matter 1.0 or so.
As shown in overloading analysis, secondly it is S9, S7, S2, S8 for S6 that maximum sensor is contributed in Leaf odor identification, other sensings
The contribution of device is smaller.Therefore, select S2 (W5S), S6 (W1S), S7 (W1W), S8 (W2S), S9 (W2W) as optimal sensor into
Row follow-up test.
As shown in Figure 2,50,100, the PCA data of 150mL processing is overlapped two-by-two.(Fig. 2 a- are analyzed by linear angles LDA
2) it finds, 100mL and 150mL processing has overlapping, and 50mL processing sampling points can be distinguished clearly.Therefore, experiment selection 50mL is
Final gas collector volume.As shown in Fig. 2 b-1,30 DEG C of sample spot compares concentration, other processing sample spot monoids in from
The degree of dissipating is big, and 50,60,70,80 DEG C of processing have overlapping.By 2b-2LDA data analyses as it can be seen that 30 DEG C and 40 DEG C of sampling point
It can understand and distinguish.Based on PCA and LDA data analyses, head space preheating temperature final choice is 30 DEG C.By Fig. 3 c data analyses
From the point of view of, 30min processing sample spot data discrete degree is small, and non-overlapping with other processing, and other several groups of processing have overlapping
Region.It is final to choose the head space time as 30min.
Embodiment 2, by taking tea leaf colorimetric detection as an example
Mature leaf is removed from non-yellow kind tea tree, be washed with deionized water rapidly it is net, dry.Utilize CM-5 types point
Light color difference meter carries out colorimetric detection.Tea leaf gradually becomes Huang with the exacerbation of nitrogen deficiency extent its surface color from green
Color.Fig. 3 shows that L* values and blade total nitrogen content linear relationship are minimum, although a* values show certain linear, but the coefficient of determination
Less than b* values.+ b* represents yellow in LAB colour systems, and+b* values show that more greatly institute's sample yellow degree is heavier.This experiment
Middle b* values are all higher than 0, linear correlation are presented, and its coefficient of determination is 0.9204, illustrates b* values with other value phases with total nitrogen content
Than having best correlation with yellowing leaf.Therefore, the mark that b* values can judge Leaf nitrogen concentration as one.
Embodiment 3, by taking the Leaf nitrogen concentration prediction model based on smell color combining as an example
It extracts response (G/G0) of above-mentioned 5 smell sensors at 80-83s and establishes prediction model with b* values.It is based on
The LTN content prediction model of smell color combining is：
In formula, G/G02、G/G06、G/G07、G/G08、G/G09W5S, W1S, W1W, W2S, W2W sensor is represented respectively to exist
Detection time is the signal response G/G0 at 80-83s.
By G/G02、G/G06、G/G08、G/G07、G/G09, b* bring above formula into, the Y value maximum group obtained is judged to the blade
Nitrogen content.
Test example 1, by taking the verification and evaluation of Leaf nitrogen concentration prediction model as an example
The modeling group and validation group of the model respectively have 80 samples.Modeling group is identical with the sample selection criteria of validation group,
Belong to similar sample and all there is identical processing, for testing, a part is used to verify a part.Following table is primarily used to test
The accuracy of the analysis nitrogen content of model of a syndrome.As shown in table 1, the accuracy rate of modeling group is 95%, and the accuracy rate of validation group is
92.5%, illustrate that the model can accurately predict the nitrogen content of blade.
The returning of Leaf nitrogen concentration prediction model of the table 1 based on smell, color is sentenced and verification result
Claims (3)
1. a kind of tea leaf nitrogen content rapid detection method based on electronic nose and spectrophotometric color measurement instrument, it is characterised in that：Step
It is as follows：
(1) 20 not damaged tea tree mature leafs are selected, cleans up, dries；
(2) the ready blade of step 1) is put into 50mL gas collection bottles, is sealed and sealed with masking foil；Using headspace sampling
Method carries out gas detection using electronic nose；Gas collection bottle is placed in progress head space preheating, head space time in 30 DEG C of environment
30min；Detection parameters are：Sensor scavenging period 100s, automatic zero set time 10s, sample preparation time 5s, between sample measures
Every time 1s, inner stream flow 300mL/min, sample introduction flow 300mL/min, using the signal at 80-83s as sensor signal point
The time point of analysis；The response G/G0 of five sensors of W5S, W1S, W1W, W2S, W2W is extracted respectively；
(3) spectrophotometric color measurement instrument detects：The ready blade of step 1) is fixed with specimen holder；Color difference meter is divided using CM-5, is adopted
With LAB colour systems under d 65 illuminant, the b of 20 blades is detected respectively*Value, takes b*It is worth average value to substitute into step 4) formula；
(4) by W5S, W1S, W1W, W2S, W2W response G/G0 and b*Value substitutes into following equation, and the Y value maximum class value obtained is sentenced
For the LTN content；
In formula：G/G02、G/G06、G/G07、G/G08、G/G09W5S, W1S, W1W, W2S, W2W sensor are represented respectively, are being detected
Time is the signal response G/G0 at 80-83s.
2. a kind of quick side of detection of tea leaf nitrogen content based on electronic nose and spectrophotometric color measurement instrument as described in claim 1
Method, it is characterised in that：The electronic nose is PEN3 type portable electric noses.
3. a kind of quick side of detection of tea leaf nitrogen content based on electronic nose and spectrophotometric color measurement instrument as described in claim 1
Method, it is characterised in that：The tea tree is conventional greenery tea tree breed.
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| CN111855757A (en) * | 2020-07-21 | 2020-10-30 | 广西壮族自治区亚热带作物研究所（广西亚热带农产品加工研究所） | Electronic nose-based Liupao tea old fragrance identification method |
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| CN111272822A (en) * | 2020-03-04 | 2020-06-12 | 浙江理工大学 | Electronic nose technology-based fabric fragrance retention lasting performance quantitative characterization method |
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| CN111855757A (en) * | 2020-07-21 | 2020-10-30 | 广西壮族自治区亚热带作物研究所（广西亚热带农产品加工研究所） | Electronic nose-based Liupao tea old fragrance identification method |
| CN111855757B (en) * | 2020-07-21 | 2023-11-14 | 广西壮族自治区亚热带作物研究所（广西亚热带农产品加工研究所） | Liupu tea aged aroma and flavor identification method based on electronic nose |
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| CN113029975A (en) * | 2021-04-06 | 2021-06-25 | 中国计量大学 | Method for identifying quality of freeze injury tea |
| CN113189145A (en) * | 2021-04-09 | 2021-07-30 | 金陵科技学院 | Method for predicting content of linalool in flower fragrance component of fresh flower based on electronic nose technology |
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